Computer Vision Engineer

As a Team Lead - Computer Vision Engineer at TechmokSa.ai specializing in AI-powered surveillance audit solutions, you will play a crucial role in developing and deploying computer vision models to analyze retail surveillance footage for various use cases. Here is a breakdown of your key responsibilities and qualifications: Role Overview: You will be responsible for designing and implementing scalable, cloud-based microservices to deliver real-time and post-event analytics for improving retail operations. Key Responsibilities: - Develop computer vision models: Build, train, and optimize deep learning models for theft detection, employee productivity monitoring, store traffic analysis, and other relevant use cases. - Microservice architecture: Design and deploy scalable microservice-based solutions for seamless integration of computer vision models into cloud or on-premise environments. - Data processing pipelines: Develop data pipelines for efficient extraction, transformation, and loading of real-time and batch video data streams. - Integration with existing systems: Collaborate with backend and frontend engineers to integrate computer vision services with POS, inventory management, and employee scheduling systems. - Performance optimization: Fine-tune models for high accuracy and real-time inference on edge devices or cloud infrastructure, optimizing for latency, power consumption, and resource constraints. - Monitoring and improvement: Continuously monitor model performance in production environments, identify issues, and implement improvements for accuracy and efficiency. - Security and privacy: Ensure compliance with industry standards for security and data privacy, particularly related to the handling of video footage and sensitive information. Qualifications: - 5+ years of experience in computer vision, including object detection, action recognition, and multi-object tracking in retail or surveillance applications. - Hands-on experience with microservices deployment on cloud platforms (AWS, GCP, Azure) using Docker, Kubernetes, or similar technologies. - Proficiency in programming languages like Python, C++, or Java. - Expertise in deep learning frameworks (TensorFlow, PyTorch, Keras) for developing computer vision models. - Strong understanding of microservice architecture, REST APIs, and serverless computing. - Knowledge of database systems (SQL, NoSQL), message queues (Kafka, RabbitMQ), and container orchestration (Kubernetes). - Familiarity with edge computing and hardware acceleration (GPUs, TPUs) for running inference on embedded devices. Additional Details: - Experience with deploying models to edge devices (NVIDIA Jetson, Coral, etc.). - Understanding of retail operations and common surveillance challenges. - Knowledge of data privacy regulations such as GDPR. - Strong analytical and problem-solving skills. - Ability to work independently and in cross-functional teams. - Excellent communication skills to convey technical concepts to non-technical stakeholders. By joining TechmokSa.ai, you will have the opportunity to shape the future of a high-impact AI startup in a collaborative and innovation-driven culture. You can expect competitive compensation, growth opportunities, and an office located in the heart of Madhapur, Hyderabad. As a Team Lead - Computer Vision Engineer at TechmokSa.ai specializing in AI-powered surveillance audit solutions, you will play a crucial role in developing and deploying computer vision models to analyze retail surveillance footage for various use cases. Here is a breakdown of your key responsibilities and qualifications: Role Overview: You will be responsible for designing and implementing scalable, cloud-based microservices to deliver real-time and post-event analytics for improving retail operations. Key Responsibilities: - Develop computer vision models: Build, train, and optimize deep learning models for theft detection, employee productivity monitoring, store traffic analysis, and other relevant use cases. - Microservice architecture: Design and deploy scalable microservice-based solutions for seamless integration of computer vision models into cloud or on-premise environments. - Data processing pipelines: Develop data pipelines for efficient extraction, transformation, and loading of real-time and batch video data streams. - Integration with existing systems: Collaborate with backend and frontend engineers to integrate computer vision services with POS, inventory management, and employee scheduling systems. - Performance optimization: Fine-tune models for high accuracy and real-time inference on edge devices or cloud infrastructure, optimizing for latency, power consumption, and resource constraints. - Monitoring and improvement: Continuously monitor model performance in production environments, identify issues, and implement improvements for accuracy and efficiency. - Security and privacy: Ensure compliance with industry standards for security and da